where \(\hat{s}\)  is a random interpolation between real and generated samples, a sampling which is motivated by the fact that an optical critic has gradient norm 1 at these coupled points [1]}. \(\lambda \)  is a regularization hyperparameter of the Lipschitz constraint.
The loss of the second critic \(L_{D_2}\)  is identical to the first critic loss \(L_{D_1}\) , except that we replace the set of the SMPL parameters \(s\)  with the set of the rendered UV images \(R(M(s))\) , where \(M(.)\)  is the SMPL function and \(R(.)\)  is the rendering function.
The total critic loss comprises the loss terms of both critics: \(L_D=L_{D_1} + L_{D_2}\)  and both terms are weighted equally.
